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Symphony RetailAI Xcelerate Forum: AI Up Front And Center

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You’d have to be living under a rock to have avoided all the recent hype associated with artificial intelligence (“AI”). The data analytical capabilities of the technology promise everything from helping medical researchers revolutionize cancer research, to helping the IRS find tax cheaters, to helping e-retailers create more relevant experiences for online shoppers, and even to help call centers find “the next best action” when handling a customer call. RSR’s own research has shown that retailers have very high expectations of AI; in a recent benchmark study, 41% of retailers declared that AI “will transform every part of our business”.

Clearly, there’s a lot going on when it comes to AI enablement. But there are few companies that are so AI-forward that they have put “AI” into the company’s name. One retail technology firm that has done exactly that is Symphony RetailAI. The company announced its formation in 2018, a merger of two companies, Symphony GOLD and Symphony EYC.

The company, whose solution portfolio is focused primarily on the fast moving consumer goods industry (grocers, convenience, and retail drug stores), is truly all-in when it comes to using AI to modernize – even revolutionize – core retail processes. I had a chance to talk to the company’s CEO, Dr. Pallab Chatterjee at the Xcelerate forum in Dallas earlier this month, about the company’s plans to revolutionize the category management process. The process, which was first articulated in the 1980’s by Prof. Brian Harris, is used by grocers and their trading partners to facilitate merchandise planning. Although technology has been applied over the years, it’s still largely a spreadsheet based activity. Most importantly, category managers are narrowly focused on their assigned product categories, not necessarily on how decisions made can affect to performance of the whole store.

Dr. Chatterjee and the data scientists at Symphony RetailAI want to change that, to infuse the category management process with insights derived from AI models to not only help planners choose the best products, prices, promotions, and product placements, but also to be able to model how changes in one product in a category can affect other areas of the store and even impact the store’s overall performance. As he explained it, I visualized a “whole store halo effect” of individual decisions. The CEO assured me that the industry will be hearing more about this initiative in the next year.

That’s the vision. But right now, many retailers are considering applying AI technologies to the demand forecasting process. The reasons are many, but it all boils down to two things: retailers recognize the need to more accurately match assortments to consumer demand, and retailers know their response to changes in demand has to be faster and more focused. The good news for retailers is first that the data to help them to be more accurate and more responsive is available, and secondly, the technology to glean insights from that new data is proven and in the marketplace right now.

The AI-enabled Forecasting Platform

At the Xcelerate conference, I had an opportunity to hear Patrick Buellet, Symphony RetailAI’s Chief Strategy Officer, share an update on the company’s demand forecasting product. Buellet started out by identifying the “elephant in the room” problem with forecasting today: bad data. According to the technologist, “bad data is 90% of the problem” with traditional forecasting. For the record, RSR’s own research shows the same thing; in our March 2019 study Mastering The Art Of Merchandising In The Technology Age, retailers identified “data is not clean: pricing, inventory, customer or POS” as the top organizational inhibitor standing in the way of integrated merchandising processes. Patrick stated that one outcome of dirty data and the resultant forecast is that 65% of orders end up requiring manual intervention.

According to Patrick, the difference between traditional forecasting vs. Symphony RetailAI’s AI-enabled forecasting platform is that the new solution is “self cleansing, self-aggregating, self learning, <uses> automated models, <and requires> no parameter settings.” When it comes to “dirty data”, the forecasting system uses AI analysis to clean that data and “complete” it.

When it comes to grouping data, the AI platform helps retailers cluster stores, identify seasonal families of products, and identify and maintain parameters and thresholds. Additionally, the system can help retailers handle difficult situations such as slow movers, erratic sales, very short lifecycle products (such as fresh foods), and new product introductions.

Perhaps most importantly, the new platform analyses the effect of external factors (weather, seasonality, holidays, promotions, competition, and geo-demographic data) on the forecast. What this all means is that unlike traditional forecasting, which uses historical data and rules (what Buellet called the “program”) to produce a forecast, Symphony RetailAI’s forecasting platform analyzes those external factors plus “cleansed” historical data to produce a “program” – the AI platform establishes the rules associated with aggregation and chooses the best forecasting algorithm. In other words, the program varies based on the context, to produce a better forecast.

To put a number to the value of AI-enablement, Symphony RetailAI performed an A/B test with a client. According to the technologist, the retailer already had processes in place to address data quality issues, and as a result was able to achieve an 80% forecast accuracy. Using the Symphony RetailAI forecasting engine, the retailer was able to increase the forecast accuracy for all items regardless of their velocity and category to 95%.

AI In Context

RSR’s 2019 research showed that over-performers (“Retail Winners”) are thinking opportunistically about how new data and AI analytics will help them to:

  • Improve their ability to adjust to deviations from sales forecasts
  • Optimize Prices and markdowns to boost sell through
  • Better incorporate customer segmentation & preferences into the planning process
  • Have a consistent, accurate and detailed demand forecasting platform
  • Tailor assortments to customer preferences
  • Integrate planning with cross-functional teams
  • Shift to a holistic pricing, assortment and promotion decision-making process

Symphony RetailAI is clearly all-in on making those objectives achievable for its customers. Of course, the company is not alone; AI seems to be everywhere right now. As my RSR partner Steve Rowen said when we summarized the highlights of the NRF 2019 “Big Show” back in January, “AI seems to be the buzzword of the year… The technology has real applications, but gets lost in the shuffle of futurespeak”.

As it is with any new and promising foundational technology, aggressively adopting new capabilities and getting viable solutions out into the marketplace creates competitive advantage. That is clearly Symphony RetailAI’s strategy, and according to the customers who attended the Dallas conference, it’s working.